한국ITS학회 논문지 (The Journal of The Korea Institute of Intelligent Transport Systems)

  • 제21권6호
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  • Pages.132-145
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  • 2022
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  • 1738-0774(pISSN)
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  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
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블랙박스 영상 기반 고속도로 사고유형 분류 및 사고 심각도 예측 평가

Classification and Prediction of Highway Accident Characteristics Using Vehicle Black Box Data

  • 조준한 (삼성교통안전문화연구소) ;
  • 이성준 (한양대학교 교통물류공학과) ;
  • 박성민 (한양대학교 교통물류공학과) ;
  • 박준영 (한양대학교 교통물류공학.스마트시티공학과 )
  • Junhan, Cho (Samsung Traffic Safety Research Institute) ;
  • Sungjun, Lee (Dept. of Transportation and Logistics Eng., Hanyang University) ;
  • Seongmin, Park (Dept. of Transportation and Logistics Eng., Hanyang University) ;
  • Juneyoung, Park ( Dept. of Transportation and Logistics Eng.Smart City Engineering., Hanyang University)
  • 투고 : 2022.10.04
  • 심사 : 2022.11.16
  • 발행 : 2022.12.31
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초록

본 연구는 고속도로에서 발생한 교통사고 블랙박스 영상을 기반으로 군집분석과 예측모형 비교를 수행하였다. 분석자료로 사고 직전의 도로 및 교통 상황을 파악할 수 있는 차량 주행행태, 노면 상태 등 사고 영상에서 추출이 가능한 항목을 설명변수로 활용하였다. 여러 요소에 의해 영향을 받는 교통사고 데이터의 특징을 고려하여 데이터의 이질성을 반영하는 군집분석을 활용하였다. 군집분석으로 분류된 각 군집을 사고 심각도 수준의 비율을 기준으로 나누고, 종속변수인 인명피해 수준을 반영하여 사고 예측 평가를 수행하였다. 사고 예측모형은 로짓 모형(Logit model)을 적용한 결과, 전체 데이터를 분석한 경우보다 군집분석에 의해 두 개의 사고 심각도 그룹을 분류하여 예측했을 때 우수한 예측 능력을 보여주었다. 이는 군집분석을 통한 그룹별 사고 특성과 사고 심각도를 반영하여 사고위험을 예측하는 것이 더 효과적인 것으로 판단된다. 또한 2차 사고와 같은 정차 중 추돌사고, 차로변경 중 측면 추돌사고 등이 중요한 주행행태변수로 작용하는 것으로 나타났다.

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

키워드

과제정보

본 논문은 국토교통부 자율주행기술개발혁신사업 '주행 및 충돌상황 대응 안전성 평가기술개발(21AMDP-C160637-01)' 과제 지원에 의해 수행되었습니다.

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